当前位置: X-MOL 学术Mech. Syst. Signal Process. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A semi-convex function for both constant and time-varying moving force identification
Mechanical Systems and Signal Processing ( IF 8.4 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.ymssp.2020.107062
Huanlin Liu , Ziwei Luo , Ling Yu

Abstract In recent years, many moving force identification (MFI) methods have been proposed to monitor moving forces indirectly from structural responses. However, the prior knowledge about moving forces has not been reasonably considered in the existing methods, so the identified moving forces are not accurate enough. To improve the accuracy of MFI results, a novel MFI method is proposed based on a semi-convex function in this study. Firstly, a model of a simply-supported beam subjected to moving forces is taken as an example to establish the relationship between moving forces and structural responses. Then, the prior knowledge that time-varying force component of a moving force usually fluctuates around its static weight in the time domain is adopted. Thus, the moving force is decomposed into constant and time-varying force components. The constant force component is unchanged over time and the time-varying force component is changed over time. Based on this prior knowledge, a semi-convex function is defined for MFI, which is inspired by an alternating iterative method (AIM) and regularization techniques. As a result, the semi-convex function is solved by the AIM, so the constant and time-varying force components can be respectively obtained through iterations, and the moving forces are identified from structural responses. Finally, to evaluate the effectiveness of the proposed method, both numerical simulations and experimental verifications are carried out. The identified results show that the proposed method can further improve the MFI results by comparing with the L2 regularization and the moving average Tikhonov regularization. The characteristics of the identified moving forces are consistent with that described in the given prior knowledge. Moreover, the proposed method can provide a good ability to estimate the total weight of the model car.

中文翻译:

用于恒定和时变移动力识别的半凸函数

摘要 近年来,已经提出了许多移动力识别(MFI)方法来间接监测结构响应中的移动力。然而,现有方法没有合理考虑有关运动力的先验知识,因此识别的运动力不够准确。为了提高 MFI 结果的准确性,本研究提出了一种基于半凸函数的新型 MFI 方法。首先,以受移动力作用的简支梁模型为例,建立移动力与结构响应之间的关系。然后,采用了运动力的时变力分量通常在时域中围绕其静态重量波动的先验知识。因此,移动力被分解为恒定和随时间变化的力分量。恒力分量随时间不变,时变力分量随时间变化。基于此先验知识,受交替迭代法 (AIM) 和正则化技术的启发,为 MFI 定义了半凸函数。因此,半凸函数由AIM求解,因此可以通过迭代分别获得恒定和时变的力分量,并从结构响应中识别移动力。最后,为了评估所提出方法的有效性,进行了数值模拟和实验验证。识别结果表明,与L2正则化和移动平均Tikhonov正则化相比,所提出的方法可以进一步提高MFI结果。识别出的移动力的特性与给定的先验知识中描述的一致。此外,所提出的方法可以提供很好的估计模型车总重量的能力。
更新日期:2021-01-01
down
wechat
bug